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import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer
DESCRIPTION = """\
# Prompt Generator with Gemma 2 9B IT
Sometimes, the hardest part of using an AI model is figuring out how to prompt it effectively. To help with this, we’ve created a prompt generation tool that guides Gemma to generate high-quality prompt templates tailored to your specific tasks. These templates follow many of our prompt engineering best practices.
The prompt generator is particularly useful as a tool for solving the “blank page problem” to give you a jumping-off point for further testing and iteration.
"""
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_id = "unsloth/gemma-2-9b-it"
tokenizer = GemmaTokenizerFast.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
)
model.config.sliding_window = 4096
model.eval()
@spaces.GPU(duration=90)
def generate(
message: str,
chat_history: list[dict],
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = chat_history.copy()
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
chat_interface = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Temperature",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.6,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.2,
),
],
stop_btn=None,
examples=[
["Hello there! How are you doing?"],
["Can you explain briefly to me what is the Python programming language?"],
["Explain the plot of Cinderella in a sentence."],
["How many hours does it take a man to eat a Helicopter?"],
["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
],
cache_examples=False,
type="messages",
)
with gr.Blocks(css="style.css", fill_height=True) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch() |